Data & Analytics Skills

When the user wants to analyze website traffic sources, attribution, or dark traffic. Also use when the user mentions "traffic sources," "dark traffic," "direct traffic," "UTM parameters," "traffic attribution," "channel attribution," "attribution optimization," "channel analysis," "traffic analysis," "traffic diversification," "natural traffic benchmark," or "organic vs paid traffic." For GA4 setup, use analytics-tracking.

When the user wants to set up, audit, or optimize analytics tracking (GA4, events, conversions). Also use when the user mentions "Google Analytics," "GA4," "event tracking," "conversions," "attribution model," "gtag," "data layer," "GA4 setup," "conversion tracking," "event setup," "User ID tracking," or "CTA attribution." For traffic insights, use traffic-analysis.

Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas

> Export Administration Regulations (EAR, 15 CFR Parts 730-774) compliance advisor — ECCN classification across all 10 CCL categories and 5 product groups (A-E), EAR99 determination, jurisdiction analysis (EAR vs ITAR order of review), license requirement analysis via Country Chart, all license exceptions (LVS, GBS, CIV, TMP, RPL, GOV, TSU, ENC, TSR, APP, BAG, AVS, ACE), end-user/end-use controls (Entity List, Denied Persons List, Unverified List, MEU List), deemed export rules, Foreign Direct Product Rule (FDPR), de minimis thresholds, 10 General Prohibitions, SNAP-R license applications, voluntary self-disclosure, civil/criminal penalties, Export Compliance Program (ECP) design, and EAR vs ITAR jurisdiction determination. Use for any dual-use export control, CCL classification, or BIS compliance question.

When the user wants to plan, design, or implement an A/B test or experiment. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," or "hypothesis." For tracking implementation, see analytics-tracking.

When the user wants to set up, improve, or audit analytics tracking and measurement. Also use when the user mentions "set up tracking," "GA4," "Google Analytics," "conversion tracking," "event tracking," "UTM parameters," "tag manager," "GTM," "analytics implementation," or "tracking plan." For A/B test measurement, see ab-test-setup.

> Automatically evaluate and compare multiple AI models or agents without pre-existing test data. Generates test queries from a task description, collects responses from all target endpoints, auto-generates evaluation rubrics, runs pairwise comparisons via a judge model, and produces win-rate rankings with reports and charts. Supports checkpoint resume, incremental endpoint addition, and judge model hot-swap. Use when the user asks to compare, benchmark, or rank multiple models or agents on a custom task, or run an arena-style evaluation.

> Use when the user has run multiple evaluation skills and wants a comprehensive analysis — maturity assessment, cross-skill signals, trends, prioritized actions, and an executive summary. Also use when the user mentions eval health check, evaluation audit, ship readiness, evaluation maturity, or "how good is my evaluation system itself." This is a read-only analysis skill.

> Build custom LLM evaluation pipelines using the OpenJudge framework. Covers selecting and configuring graders (LLM-based, function-based, agentic), running batch evaluations with GradingRunner, combining scores with aggregators, applying evaluation strategies (voting, average), auto-generating graders from data, and analyzing results (pairwise win rates, statistics, validation metrics). Use when the user wants to evaluate LLM outputs, compare multiple models, design scoring criteria, or build an automated evaluation system.

Portfolio-level agency dashboard aggregating health metrics across all client brands — campaign status, budget pacing, KPI attainment, team utilization. Use when reviewing cross-brand portfolio health, preparing for agency leadership standups, or getting a single-view snapshot of all client accounts.

Analyze marketing performance. Use when: KPI frameworks, attribution modeling, anomaly investigation, measurement strategy.

Run multi-touch attribution analysis. Use when: first/last-touch, linear, time-decay, position-based revenue allocation.

Generate client-facing reports. Use when: white-labeled performance report with KPIs, trends, strategic recommendations.

Import data from external sources. Use when: loading CRM contacts, email lists, or campaign data from CSV, JSON, or Sheets.

Generate C-suite executive summaries. Use when: preparing board reports, portfolio ROI, or strategic reviews.

Design executive marketing dashboards. Use when: building CMO reports, board metrics, or leadership views.

Query and interpret the new Google Search Console AI Performance Report (AI Overviews + AI Mode impressions/pages/countries/devices/dates). Use when: baselining AI search visibility, reading the new GSC AI report, or configuring the in-Search-Console AI opt-out toggle.

Import leads into CRM. Use when: loading leads from forms, CSV, or manual entry with deduplication and scoring.

Create live Looker Studio dashboards. Use when: connecting marketing data sources with auto-configured visualizations.

Pull live marketing metrics for a performance snapshot: KPIs vs targets, trend comparison, and cross-platform overview. Use when checking current marketing performance, monitoring KPI health, comparing to benchmarks, or getting a quick status update across analytics platforms.

Generate performance reports. Use when: tracking KPIs, trend analysis, anomaly detection, and actionable recommendations.

Generate quality trends report. Use when: reviewing eval scores over time, content type performance, or regression alerts.

Deliver performance reports. Use when: sending KPI summaries via Slack, email, or Google Sheets with analysis.

Verify marketing claims. Use when: cross-checking statistics, awards, certifications, or performance claims with sources.

> Analyze PostHog insights, dashboards, or teams beyond the current project by querying the prod Postgres replicas synced into the dogfood data warehouse (US project 2, "PostHog App + Website"). Use when asked to analyze insights across all teams or projects, another team's insights, or fleet-wide insight/dashboard usage — cases where `system.insights` only returns the current project's rows and the agent would otherwise report the data as inaccessible. Covers the synced table names for US and EU and the column-verification workflow.

Guide for adding MCP UI apps — interactive visualizations that render tool results in MCP clients like Claude Desktop. Use when adding a new detail or list view for an MCP tool, creating view components in products/*/mcp/apps/, or linking tools to UI apps via YAML.

> Guides PostHog engineers through dashboard widget platform work — ship a new widget_type (WIDGET_REGISTRY, catalog, run_widgets, WidgetCard) or update a shipped type (config, query, layout, RBAC, tile filter bar, list footer, titleHref, throttles). error_tracking_list, session_replay_list, widgetFilters, formatWidgetListCountFooter, widget_query_throttle, or WidgetCard composition. New types need widget-intake confirmation first. Not for MCP batch-add of existing types or adding tiles to a dashboard.

How to instrument PostHog's own Metrics product from PostHog-owned code — record counters, gauges, and histograms that land in posthog.metrics, the same way customers do. Use when adding application metrics in this monorepo (web, Celery, Temporal), when asked to push or ship metrics into posthog metrics, or when unsure whether the SDK in this environment supports posthog.metrics yet. Covers the environment decision (SDK-first per the public docs, OTel fallback when the SDK path is not available), the exact version gates per SDK, what is already wired internally, and how to validate metrics actually arrive.

> the in-app conversations product and their Zendesk mirrors, classify each into a diagnostic shape (frontend crash, "two numbers don't match", traffic count drop, tracker not loading / undercounting vs a competitor, ad-platform integration error, channel type misclassification), run the matching playbook, and produce reply drafts plus fix PRs where warranted. Use when asked to triage the web analytics support channel, investigate a web analytics Zendesk or conversations ticket, or explain metric discrepancies a data; never copy customer names or their traffic numbers into public artifacts (PRs, issues, commits).

> Helps PostHog engineers and agents pick the right chart when a request is about visualizing change over time, before/after comparisons, or period-over-period movement while building UI with @posthog/quill-charts (dashboards, reports, the mcp_analytics frontend, custom visualizations). Use when a user asks how something "changed", "moved", "grew", "dropped", "improved", or "regressed" between two points or periods, or mentions "before/after", "period over period", "week over week", "delta", "slope graph", "slopegraph", or comparing many series across two snapshots. Surfaces both the `SlopeChart` quill component and the native `ChartDisplayType.SlopeGraph` insight display as options.

> capture, openExternal, navigate), the data hierarchy (saved insights first, typed query nodes second, inline HogQL last), per-insight-type result shapes, date-range wiring, and event capture from a canvas. Use whenever a canvas shows metrics, charts, tables, or any PostHog data, or needs to send analytics events.

> Build reusable activation models — an activation-rate metric and a per-user/per-account activated flag — on either PostHog data-warehouse views (HogQL) or an external dbt project. Use when the user wants to define, model, or measure activation, the "aha moment", onboarding success, or which early actions predict a retention-validated combination of early actions, chosen by balancing reach (enough users hit it) against predictive power (those who hit it retain much better). Covers finding candidate actions, validating them against retention lift, count thresholds and action combinations, per-product and B2B group-level activation, and modeling the winning definition as a durable activated-flag + activation-rate model. Read modeling-warehouse-foundations first; composes modeling-product-usage-metrics for the retention validation.

> Build reusable conversion models — funnel/step conversion rates, drop-off, and time-to-convert — on either PostHog data-warehouse views (HogQL) or an external dbt project. Use when the user wants to model, define, or compute a conversion rate, funnel, step completion, drop-off, activation-funnel, signup-to-paid, or any "what % of users who did A went on to do B (within N days)" metric. Covers the funnel model (ordered steps, the conversion-window time-box, strict vs any-order), the person-vs-group aggregation unit, overall vs step-to-step conversion (two different numbers), breakdown attribution, and when a saved funnel insight beats a warehouse view. On PostHog, model funnels in HogQL with windowFunnel; in dbt, stage the event stream and compute an fct_conversion mart with tests. Read modeling-warehouse-foundations first for the view-vs-dbt mechanics; pairs with query-funnel for interactive analysis.

> Build reusable product-usage and engagement models — retention, stickiness, and lifecycle — on either PostHog data-warehouse views (HogQL) or an external dbt project. Use when the user wants to model, define, or compute whether users come back (retention / churn), how frequently they engage (stickiness / power users / DAU-WAU-MAU ratio), or the composition of the active base (new / returning / resurrecting / dormant lifecycle). These three are one engagement family sharing a start-event/return-event vocabulary and an return-rate cohort matrix, stickiness for the frequency distribution, lifecycle for growth quality. On PostHog, model them in HogQL (mirroring query-retention / query-stickiness / query-lifecycle); in dbt, build fct_retention / fct_stickiness / fct_lifecycle marts with tests. Read modeling-warehouse-foundations first; feeds the retention validation used by modeling-activation-metrics.

> Build reusable revenue models — MRR, ARR, gross revenue, new/expansion/contraction/churn, ARPU, LTV, and per-customer/per-account revenue — on either PostHog data-warehouse views (HogQL) or an external dbt project. Use when the user wants to model, define, or compute recurring revenue, monthly/annual recurring revenue, churn or retention of revenue, lifetime value, average revenue per user, or revenue by customer, cohort, product, or currency. On PostHog, build on the managed revenue_analytics_* views (revenue_item, mrr, customer, subscription, charge, product) fed by Stripe or custom revenue events — not raw Stripe tables — and normalize money with convertCurrency(). In dbt, stage the payment source and compute fct_mrr / fct_revenue_item / dim_customer marts with tests. Covers picking the right source, the subscription-config gotcha that leaves MRR empty, currency handling, and linking revenue to persons/groups. Read modeling-warehouse-foundations first for the view-vs-dbt mechanics.

> data-warehouse views / materialized views (HogQL, via the view-* MCP tools), or an external dbt project (sources.yml + staging/marts + schema tests) run against your own or PostHog's managed warehouse. Read before authoring any specific business model — covers the PostHog-vs-dbt decision, the view-create → view-materialize → sync_frequency workflow and the HogQL column-aliasing rule, the dbt project skeleton and the honest "no native dbt integration" picture, warehouse joins and star-schema dimensions, currency conversion with convertCurrency(), and checking/registering models in the data catalog for reuse. Companion to the domain skills modeling-revenue-metrics, modeling-conversion-metrics, modeling-activation-metrics, modeling-product-usage-metrics, and modeling-dimension-tables. Use when the user asks how to build a view, materialized view, or dbt model in PostHog, or which of the two stacks to use.

> Debug, support, and build PostHog MCP Analytics — product analytics for MCP servers (the `@posthog/mcp` and `posthog.mcp` SDKs plus the mcp_analytics product). Use when MCP analytics data looks wrong or missing ("events aren't showing", "intent clusters are empty", "sessions are missing", "per-tool numbers look wrong"), when writing queries over `$mcp_*` events by hand, or when doing feature work on the SDKs, the dashboard and its query runners, the self-instrumented MCP server, the `wizard mcp-analytics` install command, or the in-app onboarding. Covers the repo map, the `$mcp_*` vocabulary and where each property comes from, the rules that silently corrupt metrics when ignored, the end-to-end pipeline and where each stage breaks, and which repo to change. For reading the data rather than fixing it, prefer the `exploring-mcp-*` and `improving-mcp-tools` skills.

> Signals scout for per-account product-mix shifts. Watches each staked account's usage and forecasted MRR at the product grain for one product dropping or spiking against its own same-weekday trailing baseline while the account total holds — the shape account-level monitoring is blind to. Sweeps account notes, channel summaries, and synced comms for a planned-change explanation before filing, and files each validated shift as a report in the inbox.

> Signals scout for PostHog Customer analytics (Accounts). Watches per-account engagement for churn-risk shapes — engagement cliffs, dormancy, champion departure — and the expansion inverse, weighted by commercial ownership, and files each validated risk as a report in the inbox.

> Use this skill to verify that PostHog instrumentation is firing correctly on a website. Drives a real browser at one or more URLs, observes which PostHog events actually arrive, and reports a pass/fail summary. Use after installing the PostHog SDK on a site, after a deploy that touches tracking code, or when events appear missing in the PostHog dashboard.

Expert knowledge for Azure Carbon Optimization development including troubleshooting, security, and integrations & coding patterns. Use when using Carbon Service REST API, Python exports, RBAC roles, emissions data quality, or dashboard issues, and other Azure Carbon Optimization related development tasks. Not for Azure Cost Management (use azure-cost-management), Azure Impact Reporting (use azure-impact-reporting), Azure Monitor (use azure-monitor), Azure Policy (use azure-policy).

Expert knowledge for Azure AI Metrics Advisor development including decision making, limits & quotas, security, configuration, and integrations & coding patterns. Use when configuring data feeds, tuning anomaly detection, managing alerts/hooks, or calling Metrics Advisor REST/SDKs, and other Azure AI Metrics Advisor related development tasks. Not for Azure AI Anomaly Detector (use azure-anomaly-detector), Azure Monitor (use azure-monitor), Azure Stream Analytics (use azure-stream-analytics).

Expert knowledge for Azure Stream Analytics development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when building jobs with Event Hubs/Kafka, Cosmos DB/SQL outputs, ML/AML integration, IoT Edge, or Power BI, and other Azure Stream Analytics related development tasks. Not for Azure Synapse Analytics (use azure-synapse-analytics), Azure Data Factory (use azure-data-factory), Azure Event Hubs (use azure-event-hubs), Azure Data Explorer (use azure-data-explorer).

Expert knowledge for Azure Synapse Analytics development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when designing Synapse workspaces, SQL/Spark pools, PolyBase ELT, Synapse Link, or Purview/ML integrations, and other Azure Synapse Analytics related development tasks. Not for Azure Data Factory (use azure-data-factory), Azure Data Explorer (use azure-data-explorer), Azure Databricks (use azure-databricks), Azure HDInsight (use azure-hdinsight).

Analyze files and get detailed metadata including size, line counts, modification times, and content statistics. Use when users request file information, statistics, or analysis without modifying files.

Create HTML dashboards with KPI metric cards, bar/pie/line charts, progress indicators, and data visualizations. Use when users request dashboards, metrics displays, KPI visualizations, data charts, or monitoring interfaces.

Create HTML flowcharts and process diagrams with decision trees, color-coded stages, arrows, and swimlanes. Use when users request flowcharts, process diagrams, workflow visualizations, or decision trees.

Create HTML timelines and project roadmaps with Gantt charts, milestones, phase groupings, and progress indicators. Use when users request timelines, roadmaps, Gantt charts, project schedules, or milestone visualizations.